A Review of Point Cloud-Based 3D Object Detection Methods for Industrial Inspection

Abstract

With the continuous advancement of industrial automation, point cloud-based 3D inspection technologies have been playing an increasingly important role in product defect detection, assembly verification, and quality control. Point cloud sensors capture the three-dimensional geometric structure of objects, offering higher robustness against interference and greater localization accuracy compared with traditional 2D vision-based inspection. This paper provides a systematic review of 3D object detection methods based on point cloud data, with a particular focus on mainstream approaches such as voxelization methods, direct point-based processing, projection-based methods, and hybrid techniques. We conduct a comparative analysis of these approaches in terms of their applicability and limitations within industrial scenarios. Furthermore, we discuss the major challenges faced in industrial inspection, including high-precision requirements, sensor noise and occlusion, real-time processing demands, and limited data availability—as well as corresponding solutions such as multi-view scanning, multimodal fusion, sparse convolution acceleration, and deep learning optimization. We conclude by identifying the present challenges in this field of study and suggesting possible paths for future advancement.